Introduction

Deregulated pathways identified from transcriptome data of two sample groups have played a key role in many genomic studies. Gene-set enrichment analysis (GSEA) has been commonly used for pathway or functional analysis of microarray data, and it is also being applied to RNA-seq data. However, most RNA-seq data so far have only small replicates. This enforces to apply the gene-permuting GSEA method (or preranked GSEA) which results in a great number of false positives due to the inter-gene correlation in each gene-set. We demonstrate that incorporating the absolute gene statistic in one-tailed GSEA considerably improves the false-positive control and the overall discriminatory ability of the gene-permuting GSEA methods for RNA-seq data. To test the performance, a simulation method to generate correlated read counts within a gene-set was newly developed, and a dozen of currently available RNA-seq enrichment analysis methods were compared, where the proposed methods outperformed others that do not account for the inter-gene correlation. Analysis of real RNA-seq data also supported the proposed methods in terms of false positive control, ranks of true positives and biological relevance. An efficient R package (AbsFilterGSEA) coded with C++ (Rcpp) is available from CRAN.

Publications

  1. Improving Gene-Set Enrichment Analysis of RNA-Seq Data with Small Replicates.
    Cite this
    Yoon S, Kim SY, Nam D, 2016-01-01 - PLoS ONE

Credits

  1. Sora Yoon
    Developer

    School of Life Sciences, Ulsan National Institute of Science and Technology

  2. Seon-Young Kim
    Developer

    Department of Bioinformatics, University of Science and Technology

  3. Dougu Nam
    Investigator

    Department of Mathematical Sciences, Ulsan National Institute of Science and Technology

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Summary
AccessionBT000344
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesR
User InterfaceTerminal Command Line
Download Count0
Submitted ByDougu Nam